What are the different types of cross-validation in machine learning?

What are the different types of cross-validation in machine learning?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset. The main types include k-fold, stratified k-fold, leave-one-out, and time series cross-validation.

Can you explain k-fold cross-validation?

Sure! In k-fold cross-validation, the dataset is divided into 'k' subsets. The model is trained on 'k-1' subsets and tested on the remaining subset. This process is repeated 'k' times, with each subset used as the test set once.

What is stratified k-fold cross-validation?

Stratified k-fold cross-validation ensures that each fold has the same proportion of class labels as the entire dataset. This is particularly useful for imbalanced datasets.

How does leave-one-out cross-validation work?

Leave-one-out cross-validation is a special case of k-fold where 'k' equals the number of data points. Each iteration uses all but one data point for training and tests on the single excluded point.

When should I use time series cross-validation?

Time series cross-validation is used when the data is sequential, like stock prices. It respects the temporal order of observations, ensuring that future data is not used to predict past data.